Communication Optimization In Intelligent Reflecting Surface Aided Wireless Systems

With the growing demand for high-speed networks, 5G cellular systems provide advantages by utilizing higher frequency bands and wide bandwidth, and supporting high data transmission speeds. However, with the move to operate at high frequencies, obstacles such as buildings in urban areas and other blockages lead to challenges regarding received signal quality in wireless communications. More specifically, high-frequency signals emitted by antennas at the 5G base station (BS) can only propagate over relatively short distances toward users’ devices before being blocked by dense buildings in the area or scattered in different directions. In order to enhance the received signal strength at users’ devices, this thesis analyzes intelligent reflecting surfaces (IRS) that reflect the lens antenna beams toward users’ devices in a single-user scenario

Communication Optimization In Intelligent Reflecting Surface Aided Wireless Systems

With the growing demand for high-speed networks, 5G cellular systems provide advantages by utilizing higher frequency bands and wide bandwidth, and supporting high data transmission speeds. However, with the move to operate at high frequencies, obstacles such as buildings in urban areas and other blockages lead to challenges regarding received signal quality in wireless communications. More specifically, high-frequency signals emitted by antennas at the 5G base station (BS) can only propagate over relatively short distances toward users’ devices before being blocked by dense buildings in the area or scattered in different directions. In order to enhance the received signal strength at users’ devices, this thesis analyzes intelligent reflecting surfaces (IRS) that reflect the lens antenna beams toward users’ devices in a single-user scenario

Sparsifying Dictionary Learning for Beamspace Channel Representation and Estimation in Millimeter-Wave Massive MIMO

Millimeter-wave massive multiple-input-multiple-output (mmWave mMIMO) is reported as a key enabler in the fifth-generation communication and beyond. It is customary to use a lens antenna array to transform a mmWave mMIMO channel into a beamspace where the channel exhibits sparsity. Exploiting this sparsity enables the applicability of hybrid precoding and achieves pilot reduction. This beamspace transformation is equivalent to performing a Fourier transformation of the channel. A motivation for the Fourier character of this transformation is the fact that the steering response vectors in antenna arrays are Fourier basis vectors. Still, a Fourier transformation is not necessarily the optimal one, due to many reasons. Accordingly, this paper proposes using a learned sparsifying dictionary as the transformation operator leading to another beamspace. Since the dictionary is obtained by training over actual channel measurements, this transformation is shown to yield two immediate advantages. First, is enhancing channel sparsity, thereby leading to more efficient pilot reduction. Second, is improving the channel representation quality, and thus reducing the underlying power leakage phenomenon. Consequently, this allows for both improved channel estimation and facilitated beam selection in mmWave mMIMO. This is especially the case when the antenna array is not perfectly uniform. Besides, a learned dictionary is also used as the precoding operator for the same reasons. Extensive simulations under various operating scenarios and environments validate the added benefits of using learned dictionaries in improving the channel estimation quality and the beam selectivity, thereby improving the spectral efficiency.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Channel Estimation with Dynamic Metasurface Antennas via Model-Based Learning

Dynamic Metasurface Antenna (DMA) is a cutting-edge antenna technology offering scalable and sustainable solutions for large antenna arrays. The effectiveness of DMAs stems from their inherent configurable analog signal processing capabilities, which facilitate cost-limited implementations. However, when DMAs are used in multiple input multiple output (MIMO) communication systems, they pose challenges in channel estimation due to their analog compression. In this paper, we propose two model-based learning methods to overcome this challenge. Our approach starts by casting channel estimation as a compressed sensing problem. Here, the sensing matrix is formed using a random DMA weighting matrix combined with a spatial gridding dictionary. We then employ the learned iterative shrinkage and thresholding algorithm (LISTA) to recover the sparse channel parameters. LISTA unfolds the iterative shrinkage and thresholding algorithm into a neural network and trains the neural network into a highly efficient channel estimator fitting with the previous channel. As the sensing matrix is crucial to the accuracy of LISTA recovery, we introduce another data-aided method, LISTA-sensing matrix optimization (LISTA-SMO), to jointly optimize the sensing matrix. LISTA-SMO takes LISTA as a backbone and embeds the sensing matrix optimization layers in LISTA's neural network, allowing for the optimization of the sensing matrix along with the training of LISTA. Furthermore, we propose a self-supervised learning technique to tackle the difficulty of acquiring noise-free data. Our numerical results demonstrate that LISTA outperforms traditional sparse recovery methods regarding channel estimation accuracy and efficiency. Besides, LISTA-SMO achieves better channel accuracy than LISTA, demonstrating the effectiveness in optimizing the sensing matrix